&lt;title&gt;Multiscale image contrast amplification (MUSICA)&lt;/title&gt;

Vuylsteke, P.; Schoeters, Emile P.

doi:10.1117/12.175090

Cited by 68 publications

(37 citation statements)

References 10 publications

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“…Radiological image processing is no exception and it was soon realized that the concept of multi-scale processing would be able to provide the means to fine-tune local contrast amplification. The algorithm proposed by Vuylsteke and soon introduced commercially by AGFA known as Multi-Scale Contrast Amplification (MUSICA) may be considered a breakthrough in the context of medical image enhancement [9]. The multi-scale approach triggered a significant number of patents up the point that, currently, there is virtually no manufacturer without including an image enhancement algorithm that may be classified as a MUSICA variant.…”

Section: Multi-scale Processingmentioning

confidence: 99%

Image Enhancement for Digital Radiography

Lança

Silva

2012

Digital Imaging Systems for Plain Radiography

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Once in a digital form, a radiographic image may be processed in several ways in order to turn the visualization an act of improved diagnostic value. Practitioners should be aware that, depending on each clinical context, digital image processing techniques are available to help to unveil visual information that is, in fact, carried by the bare digital radiograph and may be otherwise neglected. The range of visual enhancement procedures includes simple techniques that deal with the usual brightness and contrast manipulation up to much more elaborate multi-scale processing that provides customized control over the emphasis given to the relevant finer anatomical details. This chapter is intended to give the reader a practical understanding of image enhancement techniques that might be helpful to improve the visual quality of the digital radiographs and thus to contribute to a more reliable and assertive reporting.

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Section: Multi-scale Processingmentioning

confidence: 99%

Image Enhancement for Digital Radiography

Lança

Silva

2012

Digital Imaging Systems for Plain Radiography

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“…An algorithm that uses Laplacian pyramid [5] decomposition for this purpose was proposed in [1], while approaches that use wavelet decomposition were proposed in [6] and [7]. A simplified variation of these approaches, that uses only the intensity of the coefficients obtained in the multi-scale image decomposition instead of their correlation, was used as a denoising step of medical image enhancement algorithm in [8]. Image decomposition to low-frequency and high-frequency components, and subsequent high-frequency component value manipulation was proposed in [2].…”

Section: This Paper Is a Revised And Expanded Version Of The Paper Prmentioning

confidence: 99%

“…Upon analyzing (8) and (9), it becomes clear that images , , , , are calculated recursively, e.g. is obtained by filtering in the direction of the increasing , while is obtained by filtering in the decreasing direction, etc.…”

Section: Recursive Anisotropic Diffusionmentioning

confidence: 99%

Recursive noise reduction for digital radiography images

Ostojić¹,

Starčević²,

Petrović³

2018

Telfor J

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Abstract-Modern digital radiography images are large, suggesting that a denoising algorithm should be computationally efficient. In this paper, we propose a homomorphic recursive noise reduction approach. The proposed method was compared to anisotropic diffusion on a database consisting of 47 clinical radiography images, and was subjectively deemed better at noise reduction. The proposed approach was shown to require 50 % fewer iterations to produce subjectively the same results. Objective comparison of the algorithms through a structural similarity index and peak signal-to-noise ratio showed that the proposed method outperforms anisotropic diffusion for various signal-to-noise ratios.Index Terms-digital radiography, homomorphic filtering, image noise reduction, recursive filtering.

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“…Interpolation, contrast enhancement, convolution, and addition operations are repeated until the reconstructed image at the original resolution level is obtained. The reconstruction is completely reversible if the interpolation filters used in decomposition and reconstruction are identical [10,[16][17][18][19].…”

Section: The Laplacian Gaussian Pyramidmentioning

confidence: 99%

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology

Zhu

Yang

et al. 2013

2013 IEEE International Conference on Information and Automation (ICIA)

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A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

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<title>Multiscale image contrast amplification (MUSICA)</title>

Cited by 68 publications

References 10 publications

Image Enhancement for Digital Radiography

Image Enhancement for Digital Radiography

Recursive noise reduction for digital radiography images

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology

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